c19 IFR vs normal mortality – https://twitter.com/ikashnitsky/status/1483047960791457794

c19-ifr-vs-hmd.R

#===============================================================================
# 2022-01-17 -- twitter
# c19 IFR vs normal mortality
# Ilya Kashnitsky, ilya.kashnitsky@gmail.com, @ikashnitsky
#===============================================================================

library(tidyverse)
library(magrittr)
library(hrbrthemes)
library(reticulate)

options(scipen = 999)

# load the ifr data collected by @zorinaq
# https://twitter.com/zorinaq/status/1330631088935370753
# https://github.com/mbevand/covid19-age-stratified-ifr/blob/master/covid_vs_flu.py
# I copied the lines taht create IFR data object in a separate gist file
source_python("https://gist.githubusercontent.com/ikashnitsky/800e46910d07942093086b6b148d1d05/raw/18b1de1a47d1d0965124376f282b8652362effb1/zorinaq.py")

# simplify this list
foo <- ifrs_covid %>% unlist()

avg_vec <- function(x) {x %>% as.numeric %>% mean}

# turn into a data frame
ifr <- tibble(
    name = foo %>% str_remove_all("[^\\p{L}']+") %>% na_if(""),
    age = names(foo),
    values = as.numeric(foo)
) %>%
    fill(name, .direction = "down") %>%
    drop_na() %>%
    # calculate central age
    mutate(
        central_age = age %>% str_extract_all("[:alnum:]+") %>%
            map(as.numeric) %>% map(mean) %>% unlist()
    )


# !!! The code below assumes that one has full HMD downloaded and unpacked locally. To get a copy of it go to
# https://www.mortality.org/cgi-bin/hmd/hmd_download.php
# and choose "All statistics for HMD", currently 160326 Kb
# Ilya: my local copy was downloaded on 2021-02-02

# functions to read local HMD directories
devtools::source_gist("0f93062f2b67eeac69949554027fa84f")

# the local HMD path (Ilya)
hmdpath <- fs::as_fs_path("~/data/hmd/")

# life tables
lt1x1 <- bind_rows(
    b = path(hmdpath, "lt_both", "bltper_1x1") %>% fread_hmd_dir(),
    f = path(hmdpath, "lt_female", "fltper_1x1") %>% fread_hmd_dir(),
    m = path(hmdpath, "lt_male", "mltper_1x1") %>% fread_hmd_dir(),
    .id = "sex"
)


lt1x1 %>%
    group_by(country) %>%
    filter(year == last(year)) %>%
    ungroup() %>%
    filter(sex == "b") %>%
    ggplot()+
    stat_smooth(
        aes(age, qx * 100, group = country), color = "#ffcdd277",
        se = F, method = "loess", span = .2
    )+
    scale_y_continuous(
        trans = "log10",
        breaks = 10^(-3:2),
        labels = c(".001", ".01", ".1", "1", "10", "100")
    )+
    scale_x_continuous(breaks = seq(0, 110, 10))+
    geom_point(data = ifr, aes(central_age, values), color = "#df536b")+
    geom_smooth(data = ifr, aes(central_age, values), color = "#df536b", se = F)+
    theme_minimal(base_family = font_rc)+
    theme(
        line = element_line(lineend = "round"),
        panel.grid.minor = element_blank(),
        plot.title = element_text(family = "Roboto Slab", face = 2)
    )+
    labs(
        title = "COVID-19 IFR vs baseline yearly mortality",
        caption = "Data @HMDatabase; IFR collected by @zorinaq; design @ikashnitsky",
        y = "Death probability, %", x = "Age"
    )+
    annotate(
        "text", label = c("Countries of HMD\n(last available year)", "COVID-19\nInfection Fatality Ratios"), 
        x = c(5, 45), y = c(7, .007), hjust = 0, vjust = 1,
        family = "Roboto Slab", lineheight = .9, color = c("#ffcdd2", "#df536b")
    )

zorinaq.py

# Age-stratified IFR estimates for COVID-19
ifrs_covid = [

    # Calculated from Spanish ENE-COVID study
    # (see calc_ifr.py)
    ('ENE-COVID', {
        (0,9):    0.003,
        (10,19):  0.004,
        (20,29):  0.015,
        (30,39):  0.030,
        (40,49):  0.064,
        (50,59):  0.213,
        (60,69):  0.718,
        (70,79):  2.384,
        (80,89):  8.466,
        (90,99): 12.497,
    }),

    # US CDC estimate as of 19 Mar 2021
    # https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html
    # (table 1)
    ('US CDC', {
        (0,17):   0.002,
        (18,49):  0.05,
        (50,64):  0.6,
        (65,99): 9.0,
    }),

    # Verity et al.
    # https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30243-7/fulltext
    # (table 1)
    ('Verity', {
        (0,9):    0.00161,
        (10,19):  0.00695,
        (20,29):  0.0309,
        (30,39):  0.0844,
        (40,49):  0.161,
        (50,59):  0.595,
        (60,69):  1.93,
        (70,79):  4.28,
        (80,99): 7.80,
    }),

    # Levin et al.
    # https://link.springer.com/article/10.1007/s10654-020-00698-1
    # (table 3)
    ('Levin', {
        (0,34):   0.004,
        (35,44):  0.068,
        (45,54):  0.23,
        (55,64):  0.75,
        (65,74):  2.5,
        (75,84):  8.5,
        (85,99): 28.3,
    }),

    # Salje et al.: Estimating the burden of SARS-CoV-2 in France
    # https://science.sciencemag.org/content/369/6500/208
    # Supplementary Materials:
    # https://science.sciencemag.org/content/sci/suppl/2020/05/12/science.abc3517.DC1/abc3517_Salje_SM_rev2.pdf
    # (table S2)
    ('Salje', {
        (0,19):   0.001,
        (20,29):  0.005,
        (30,39):  0.02,
        (40,49):  0.05,
        (50,59):  0.2,
        (60,69):  0.7,
        (70,79):  1.9,
        (80,99):  8.3,
    }),

    # Perez-Saez et al.
    # https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30584-3/fulltext
    ('Perez-Saez', {
        (5,9):    0.0016,
        (10,19):  0.00032,
        (20,49):  0.0092,
        (50,64):  0.14,
        (65,99): 5.6,
    }),

    # Picon et al.
    # https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7493765/
    # (table 2)
    ('Picon', {
        (20,39):  0.08,
        (40,59):  0.24,
        (60,99):  4.63,
    }),

    # Poletti et al.
    # https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.31.2001383
    # (table 1, column "Any time")
    ('Poletti', {
        (0,19):   0,
        (20,49):  0,
        (50,59):  0.46,
        (60,69):  1.42,
        (70,79):  6.87,
        (80,99):  18.35,
    }),

    # Gudbjartsson et al.: Humoral Immune Response to SARS-CoV-2 in Iceland
    # https://www.nejm.org/doi/full/10.1056/NEJMoa2026116
    # Supplementary Appendix 1
    # https://www.nejm.org/doi/suppl/10.1056/NEJMoa2026116/suppl_file/nejmoa2026116_appendix_1.pdf
    # (table S7)
    ('Gudbjartsson', {
        (0,70):   0.1,
        (71,80):  2.4,
        (81,99): 11.2,
    }),

    # Public Health Agency of Sweden
    # https://www.folkhalsomyndigheten.se/contentassets/53c0dc391be54f5d959ead9131edb771/infection-fatality-rate-covid-19-stockholm-technical-report.pdf
    # (table B.1)
    ('PHAS', {
        (0,49):   0.01,
        (50,59):  0.27,
        (60,69):  0.45,
        (70,79):  1.92,
        (80,89):  7.20,
        (90,99):  16.21,
    }),

    # O’Driscoll et al.: Age-specific mortality and immunity patterns of SARS-CoV-2
    # https://www.nature.com/articles/s41586-020-2918-0
    # Supplementary information
    # https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-020-2918-0/MediaObjects/41586_2020_2918_MOESM1_ESM.pdf
    # (table S3)
    ('O’Driscoll', {
        (0,4):   0.003,
        (5,9):   0.001,
        (10,14): 0.001,
        (15,19): 0.003,
        (20,24): 0.006,
        (25,29): 0.013,
        (30,34): 0.024,
        (35,39): 0.040,
        (40,44): 0.075,
        (45,49): 0.121,
        (50,54): 0.207,
        (55,59): 0.323,
        (60,64): 0.456,
        (65,69): 1.075,
        (70,74): 1.674,
        (75,79): 3.203,
        (80,99): 8.292,
    }),

    # Ward et al.: Antibody prevalence for SARS-CoV-2 in England following first peak of the pandemic: REACT2 study in 100,000 adults
    # https://www.nature.com/articles/s41467-021-21237-w
    # (table 2)
    ('REACT2', {
        (15,44):  0.03,
        (45,64):  0.52,
        (65,74):  3.13,
        (75,99): 11.64,
    }),

    # Yang et al.: Estimating the infection fatality risk of COVID-19 in New York City during the spring 2020 pandemic wave
    # https://www.medrxiv.org/content/10.1101/2020.06.27.20141689v2
    # (table 1)
    ('Yang', {
        (0,24):   0.0097,
        (25,44):  0.12,
        (45,64):  0.94,
        (65,74):  4.87,
        (75,99): 14.17,
    }),

    # Molenberghs et al.: Belgian Covid-19 Mortality, Excess Deaths, Number of Deaths per Million, and Infection Fatality Rates
    # https://www.medrxiv.org/content/10.1101/2020.06.20.20136234v1
    # (table 6)
    ('Molenberghs', {
        (0,24):   0.0005,
        (25,44):  0.017,
        (45,64):  0.21,
        (65,74):  2.24,
        (75,84):  4.29,
        (85,99): 11.77,
    }),

    # Brazeau et al.
    # https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-34-ifr/
    # (table 2, column "IFR (%) with Seroreversion")
    ('Brazeau', {
        (0,4):    0.00,
        (5,9):    0.01,
        (10,14):  0.01,
        (15,19):  0.02,
        (20,24):  0.02,
        (25,29):  0.04,
        (30,34):  0.06,
        (35,39):  0.09,
        (40,44):  0.15,
        (45,49):  0.23,
        (50,54):  0.36,
        (55,59):  0.57,
        (60,64):  0.89,
        (65,69):  1.39,
        (70,74):  2.17,
        (75,79):  3.39,
        (80,84):  5.30,
        (85,89):  8.28,
        (90,99): 16.19,
    }),

]